- Perform an analysis using the geographically weighted regression technique
- Discuss the appropriateness of GWR under various conditions
- Describe the characteristics of the spatial expansion method
- Explain the principles of geographically weighted regression
- Compare and contrast GWR with universal kriging using moving neighborhoods
- Explain how allowing the parameters of the model to vary with the spatial location of the sample data can be used to accommodate spatial heterogeneity
- Analyze the number of degrees of freedom in GWR analyses and discuss any possible difficulties with the method based on your results
This knowledge area embodies a variety of data driven analytics, geocomputational methods, simulation and model driven approaches designed to study complex spatial-temporal problems, develop insights into characteristics of geospatial data sets, create and test geospatial process models, and construct knowledge of the behavior of geographically-explicit and dynamic processes and their patterns.
Topics in this Knowledge Area are listed thematically below. Existing topics are in regular font and linked directly to their original entries (published in 2006; these contain only Learning Objectives). Entries that have been updated and expanded are in bold. Forthcoming, future topics are italicized.